Glucose exposure diagnostics and therapeutics related thereto

ABSTRACT

Glucose exposure can be indicative of a number of potential underlying health conditions. The present disclosure is directed to measuring glucose exposure, determined, for example, on an hourly basis. This glucose exposure, in at least one embodiment, represents a current glucose level over the span of given period (e.g., a portion of 24 hours). If a patient&#39;s glucose exposure exceeds a glucose threshold or limit (for a 24 hour period in some embodiments), then a variety of treatments may be administered to the patient to help lower overall glucose exposure (e.g., during the 24 hour period).

BACKGROUND

Average glucose monitoring is typically measured on a 24 hour basis andover time (e.g., what is a person's average 24 hour glucose, averagedover weeks or months). This average glucose data may be used todetermine a number of factors. Such factors include, but are not limitedto, diabetes, pre-diabetes, insulin resistance, etc. Whilecontrolling/understanding average 24 hour glucose (averaged over time),more granular glucose monitoring may help control glucose throughout aday and may lead to administration of therapeutics/treatments, which mayhelp prevent or control (or better control) glucose-related ailments andissues.

Thus, there is a long-felt but unresolved need for a system or processthat continuously monitors and reports glucose levels in the context ofoverall glucose targets or limits, which may provide a diagnostic ofparticular ailments and lead to administration of treatment optionsbased on the glucose levels.

BRIEF SUMMARY OF THE DISCLOSURE

Briefly described, and according to one embodiment, aspects of thepresent disclosure generally relate to systems and processes formeasuring glucose exposure, diagnosing glucose over exposure, andtreatments for the same.

The present disclosure generally relates to diagnostics and treatmentsbased on measured glucose exposure, determined, for example, on anhourly basis. The treatments may include, but is not limited toprescribing, recommending and/or administering to the user insulin,medications for the treatment of Nonalcoholic Fatty Liver Disease and/orNonalcoholic Steatohepatitis, including insulin-sensitizing drugs,vitamin E, and/or exercise and low-carbohydrate diet, medication forpre-diabetic users, weight loss medication. This glucose exposure, in atleast one embodiment, represents a current glucose level of a user overthe span of given period (e.g., a portion of 24 hours).

For example, the present systems and processes may determine (orreceive) a 24 hour glucose target or limit and determine a glucose limitor target for a particular hour (e.g., at 10:00 AM, a user should have aglucose exposure of 1000 mg/dl, where 1000 mg/dl is a summation ofglucose from previous hours in the day). Continuing with this example,the present systems and methods may determine a glucose exposure for theuser of 1010 mg/dl by summing the glucose exposure of the user for eachhour until 10:00 AM. The present systems and methods, in this example,may display the current glucose exposure (e.g., 1010 mg/dl) as apercentage or comparison of the glucose target or limit of 1000 mg/dl,potentially indicating that the user to trending towards a glucose levelthat is over the glucose target or limit for the 24 hour period. Incertain situations, for example, if the current glucose exposure ishigher than the glucose target or limit by a certain percentage, thesystem may administer a treatment to the user to decrease the currentglucose exposure.

According to a first aspect, a method for treating glucose over exposuremay include receiving indications of glucose levels of a patient via afilament interacting with interstitial fluid on a particular interval,determining an average glucose level for a particular hour by averagingone or more indications of the glucose levels of the patient receivedduring the particular hour, determining a current glucose exposure forthe particular hour by adding the average glucose level for theparticular hour to a summation of average glucose levels of the patientfor hours preceding the particular hour in a 24-hour period, determininga target glucose level for the particular hour by multiplying a glucoseexposure limit per hour by the numerical expression of the particularhour, and if the current glucose exposure for the particular hourexceeds the target glucose level for the particular hour, administeringtreatment to the patient to lower the patient's glucose exposure levelto at or below a 24-hour glucose exposure limit before the end of the24-hour period.

According to a second aspect, the method for treating glucose overexposure of the first aspect or any other aspect, wherein the treatmentincludes insulin.

According to a third aspect, the method for treating glucose overexposure of the second aspect or any other aspect, wherein the treatmentincludes one or more of the group including rapid-acting insulin,short-acting insulin, intermediate-acting insulin, mixed-insulin, orlong-acting insulin.

According to a fourth aspect, the method for treating glucose overexposure of the first aspect or any other aspect, wherein the treatmentincludes pioglitazone. According to a fifth aspect, the method fortreating glucose over exposure of the first aspect or any other aspect,wherein the treatment includes one or more weight loss drugs.

According to a sixth aspect, the method for treating glucose overexposure of the first aspect or any other aspect, wherein the treatmentincludes one or more drugs to treat nonalcoholic fatty liver disease ornonalcoholic steatohepatitis.

According to a seventh aspect, the method for treating glucose overexposure of the first aspect or any other aspect, wherein the glucoselimit per hour is based at least in part on historical glucose exposuredata for the patient.

According to an eighth aspect, the method for treating glucose overexposure of the first aspect or any other aspect, wherein the particularinterval is 15 minutes.

According to a ninth aspect, the method for treating glucose overexposure of the eighth aspect or any other aspect, wherein determiningthe average glucose level for the particular hour includes averagingfour indications of the current glucose level of the patient receivedduring the particular hour.

According to a tenth aspect, the method for treating glucose overexposure of the first aspect or any other aspect, wherein the particularinterval is 1 minute.

According to a eleventh aspect, the method for treating glucose overexposure of the tenth aspect or any other aspect, wherein determiningthe average glucose level for the particular hour includes averaging 60indications of the current glucose level of the patient received duringthe particular hour.

According to a twelfth aspect, the method for treating glucose overexposure of the first aspect or any other aspect, wherein the 24-hourglucose exposure limit includes a summation of the glucose limit perhour for 24 hours.

According to a thirteenth aspect, the method for treating glucose overexposure of the twelfth aspect or any other aspect, wherein the averageglucose levels of the patient for hours preceding the particular hour inthe 24-hour period includes at least one average glucose level based onhistorical data.

According to a fourteenth aspect, the method for treating glucose overexposure of the thirteenth aspect or any other aspect, wherein the atleast one average glucose level includes an average of average glucoselevels for an hour immediately preceding the particular hour and an hourimmediately succeeding the particular hour in a different 24-hourperiod.

According to a fifteenth aspect, the method for treating glucose overexposure of the thirteenth aspect or any other aspect, wherein the atleast one average glucose level includes an average of stored glucoseexposure data for the particular hour for different 24-hour periods.

According to a sixteenth aspect, the method of diagnosing and treatingglucose over exposure may include receiving a current glucose exposurefor a particular hour, wherein the current glucose exposure for theparticular hour is determined by adding an average glucose level for theparticular hour to a summation of average glucose levels of a patientfor hours preceding the particular hour in a 24-hour period, wherein: A)the average glucose level for the particular hour is determined byaveraging one or more indications of a glucose level of the patientreceived during the particular hour; and B) the one or more indicationsof the current glucose level of the patient are derived from a filamentinteracting with interstitial fluid, determining a target glucose levelfor the particular hour based on a glucose exposure limit, and if thecurrent glucose exposure for the particular hour exceeds the targetglucose level for the particular hour, administering treatment to thepatient to lower the patient's glucose exposure level to at or below theglucose exposure limit.

According to a seventeenth aspect, the method of diagnosing and treatingglucose over exposure of the sixteenth aspect or any other aspect,wherein the glucose exposure limit is a 24-hour glucose exposure limit.

According to a eighteenth aspect, the method of diagnosing and treatingglucose over exposure of the sixteenth aspect or any other aspect,wherein the glucose exposure limit is a glucose exposure limit for lessthan 24 hours.

According to a nineteenth aspect, the method for treating glucose overexposure of the first aspect or any other aspect, wherein the treatmentincludes one or more of the group including rapid-acting insulin,short-acting insulin, intermediate-acting insulin, mixed-insulin, orlong-acting insulin.

According to a twentieth aspect, the method for treating glucose overexposure of the first aspect or any other aspect, wherein the treatmentincludes one or more drugs to treat nonalcoholic fatty liver disease ornonalcoholic steatohepatitis.

According to a twenty-first aspect, the method for treating diabetes orpre-diabetes may include the steps of receiving indications of a currentglucose level of a patient via a filament interacting with interstitialfluid at one or more intervals, determining an average glucose level fora particular hour by averaging one or more indications of the currentglucose level of the patient over the one or more intervals receivedduring the particular hour, determining a current glucose exposure forthe particular hour by adding the average glucose level for theparticular hour to a summation of average glucose levels of the patientfor hours preceding the particular hour in a 24-hour period, determininga target glucose level for the particular hour based on a glucoseexposure limit, and if the current glucose exposure for the particularhour exceeds the target glucose level for the particular hour,administering insulin to the patient to lower the patient's glucoseexposure level.

These and other aspects, features, and advantages of the claimedinvention(s) will become apparent from the following detailed writtendescription of the preferred embodiments and aspects taken inconjunction with the following drawings, although variations andmodifications thereto may be effected without departing from the spiritand scope of the novel concepts of the disclosure.

BRIEF DESCRIPTION OF THE FIGURES

The accompanying drawings illustrate one or more embodiments and/oraspects of the disclosure and, together with the written description,serve to explain the principles of the disclosure. Wherever possible,the same reference numbers are used throughout the drawings to refer tothe same or like elements of an embodiment, and wherein:

FIG. 1 is a diagram of an exemplary glucose exposure system, accordingto one embodiment of the present disclosure;

FIG. 2A is a flow chart of an exemplary glucose exposure process,according to one embodiment of the present disclosure;

FIG. 2B is a flow chart of an exemplary target glucose leveldetermination process, according to one embodiment of the presentdisclosure;

FIG. 3A is a flow chart of an exemplary glucose exposure process,according to one embodiment of the present disclosure;

FIG. 3B is a flow chart of an exemplary target glucose level calculationprocess, according to one embodiment of the present disclosure; and

FIG. 3C is a flow chart of an exemplary glucose exposure for aparticular hour determination process, according to one embodiment ofthe present disclosure; and

FIG. 4 is a flowchart of an exemplary glucose exposure diagnostic andtreatment process, according to one embodiment of the presentdisclosure.

DETAILED DESCRIPTION

For the purpose of promoting an understanding of the principles of thepresent disclosure, reference will now be made to the embodimentsillustrated in the drawings and specific language will be used todescribe the same. It will, nevertheless, be understood that nolimitation of the scope of the disclosure is thereby intended; anyalterations and further modifications of the described or illustratedembodiments, and any further applications of the principles of thedisclosure as illustrated therein are contemplated as would normallyoccur to one skilled in the art to which the disclosure relates. Alllimitations of scope should be determined in accordance with and asexpressed in the claims.

Whether a term is capitalized is not considered definitive or limitingof the meaning of a term. As used in this document, a capitalized termshall have the same meaning as an uncapitalized term, unless the contextof the usage specifically indicates that a more restrictive meaning forthe capitalized term is intended. However, the capitalization or lackthereof within the remainder of this document is not intended to benecessarily limiting unless the context clearly indicates that suchlimitation is intended.

Overview

In various embodiments, the present systems and processes determineglucose exposure for a particular patient (also “user”) on an hourlybasis. The present systems and processes may, in at least oneembodiment, thereby diagnose when the particular patient is over glucoseexposed and administer or recommend administration of therapeutictreatments based on the determined glucose exposure. The therapeutictreatments may treat diseases such as but not limited to obesity,diabetes, nonalcoholic fatty liver disease, nonalcoholicsteatohepatitis, and pre-diabetic conversion to type-II diabetes.Treatments include, but are not limited to prescribing, recommendingand/or administering to a patient insulin, medications for the treatmentof Nonalcoholic Fatty Liver Disease and/or Nonalcoholic Steatohepatitis,exercise, low-carbohydrate meals or diets, medication for pre-diabeticusers, and/or weight loss medication. The treatments may be based on thepatient's current glucose exposure as compared to a target glucose levelfor a particular hour, such that, if the patient's current glucoseexposure exceeds the target glucose level for the particular hour, thepatient may be administered treatment.

To determine the glucose exposure, in at least one embodiment, thesystems and processes determine average glucose level for the patientfor each hour, then sum the average glucose level per hour over thenumber of hours currently passed in a given day to determine a glucoseexposure for the current time/hour (e.g., sums the average glucose from12:00 AM-1:00 AM, 1:00 AM-2:00 AM, 2:00 AM-3:00 AM, and 4:00 AM-5:00 AMto determine a glucose exposure at 5:00 AM).

In order to determine an average glucose level for an hour, in at leastone embodiment, the disclosed systems and processes: a) receive glucosedata (e.g., an indication of current glucose levels of a user) from asensor in predetermine intervals (e.g., 15 minutes); and b) averages thereceived glucose data over an hour (e.g., averages four glucoseindication taken at 15 minute intervals over the hour).

In at least one embodiment, the present systems and processes displaycurrent glucose exposure for a particular hour as a percentage of aglucose target or limit for the day and/or hour (or a limit/target for aday thus far). In some embodiments, the systems and processes may createalerts based on current glucose exposure. For example, if a user'scurrent glucose exposure is over the target or limit for the hour (e.g.,thus far in the day), then the systems and processes may recommend thatthe user embark on glucose reducing or limiting actions (e.g., go for awalk, eat a low carb lunch, etc.). In this way, the systems andprocesses may enable a user to tweak habits or actions to influenceglucose exposure during a day.

In at least one embodiment, the systems and processes may “backfill”glucose data for times when a sensor (e.g., that reads a user's glucose)is disconnected or glucose indications or readings are otherwiseunavailable (e.g., data could be corrupted, unavailable, or otherwiseunusable) via one or more processes or mechanisms discussed herein.

For example, in one embodiment, a user (or the system) may set a targetglucose limit of 1200 units of measurement (such as, e.g., milligramsper deciliter) per day. Based on the target glucose limit, the glucoseexposure system may determine at a given time the target glucose limitfor that given time, as well as the user's glucose exposure. Continuingwith the above example, if the day begins at midnight (00:00 AM) andgoes for 24 hours, then the glucose exposure limit per hour would be 50mg/dl. If the user checks his glucose exposure at 8:00 AM, the targetglucose exposure would be 400 milligrams, and the glucose exposuresystem would display the user's glucose exposure (e.g., 390 mg/dl basedon sensor indications, as discussed herein) compared to the targetglucose for 8:00 AM, to indicate to the user if the user was below, at,or above the target glucose exposure limit for 8:00 AM.

EXEMPLARY EMBODIMENTS

Referring now to the figures, for the purposes of example andexplanation of the fundamental processes and components of the disclosedsystems and methods, reference is made to FIG. 1, which illustrates anexemplary system diagram 100 of one embodiment of a glucose exposuresystem. As will be understood and appreciated, the exemplary diagram 100shown in FIG. 1 represents merely one approach or embodiment of thepresent system, and other aspects are used according to variousembodiments of the present system.

As shown in FIG. 1, according to one embodiment, the system 100 mayinclude one or more networks 102, a sensor 104, a device 106, a datacollection server 108, and a GPS satellite 110.

In various embodiments, the sensor 104 may include a glucose sensor 120and a transmitter 122. In one or more embodiments, the glucose sensor120 may be a filament that interacts with a user to derive glucose data.In some embodiments, the glucose data/information may include anindication of a glucose level of a patient, such as, but not limited to,an electronic signal. In at least one embodiment, the glucose sensor 120may interact with interstitial fluid or other bodily fluid of the userto derive the glucose data. In several embodiments, the glucose sensor120 may interact with the interstitial fluid or other bodily fluid ofthe user at a particular interval.

In multiple embodiments, the sensor 104 may also include a transmitter122. In many embodiments, the transmitter 122 may transmit the derivedglucose data from the sensor 104 to the data collection server 108 ordevice 106, via the one or more networks 102. In at least oneembodiment, the transmitter 122 may transmit via Bluetooth radio,near-field communication (NFC), and other similar wireless communicationtools.

In various embodiments, the sensor 104 receives an electronic signalfrom the glucose sensor 120 that indicates the amount of glucose in theinterstitial fluid. In many embodiments, the sensor 104 measures theelectronic signal (e.g., voltage change or the like) from the glucosesensor 120, and, from that measurement, the sensor 104 determines howmuch glucose is present, which the sensor 104 reads as glucose data. Inone embodiment, the glucose data may include an amount of glucose in theuser's interstitial fluid. In many embodiments, the amount of glucosemay be measured in a unit of measurement (e.g., milligrams perdeciliter). In one or more embodiments, the sensor 104 obfuscates theglucose data before transmitting the obfuscated glucose data, via thetransmitter 122, to the device 106 or data communication server 108. Inat least one embodiment, the sensor 104 may obfuscate the glucose databy encryption, one or more hashing algorithms, or other similarprocesses.

In at least one embodiment, the sensor 104 may receive an electronicsignal from the glucose sensor 120 that indicates the amount of glucosein the interstitial fluid of the user. In some embodiments, the sensor104 may measure the electronic signal, but may not translate theelectronic signal into a corresponding amount of glucose present. In oneor more embodiments, the sensor 104 instead may obfuscate the rawelectronic signal data by encryption, one or more hashing algorithms, orother similar processes, and transmit the raw electronic signal data tothe device 106 for translation into corresponding milligrams perdeciliter. In many embodiments, the device 106 may deobfuscate theelectronic signal data, or transmit the obfuscated electronic signaldata to the data connection server 108 to be deobfuscated. In someembodiments, once the electronic signal data is deobfuscated, the device106 or data connection server 108 may read the electronic signal dataand determine, from the electronic signal data, the amount of glucose inthe user's interstitial fluid (in milligrams per deciliter).

In a further embodiment, the sensor 104 may also create a timestamp whenreceiving the electronic signal from the glucose sensor 120, and mayassociate the timestamp with the received electronic signal or measuredvoltage change (or other electronic data). In multiple embodiments, thetimestamp data may be included in the glucose data that is obfuscatedand transmitted to the device 106. In one or more embodiments, thesensor 104 may associate an identifier with the received electronicsignal from the glucose sensor 120. In at least one embodiment, thesensor 104 may include the identifier in the glucose data, and obfuscateand transmit the identifier, along with the glucose data, to the device106.

In several embodiments, the device 106 may be a mobile device, tablet,smart watch, laptop, web application, or similar devices. In one or moreembodiments, the device 106 may be wearable by the user. In at least oneembodiment, the device 106 may include a display 112, a transmitter 114,and one or more processors 126. In many embodiments, the display 112 maydisplay the user's glucose exposure data, target glucose exposure data,a comparison between the user's glucose exposure data and target glucoseexposure data, and/or other data related thereto.

In multiple embodiments, the device 106 may receive, via one or moreradios, the glucose data, obfuscated or deobfuscated, from the sensor104 or the data collection server 108, via the one or more networks 102.In at least one embodiment, the transmitter 114 may transmit viaBluetooth radio, NFC, and other similar wireless communication tools. Inmany embodiments, one or more radios on the device 106 may receive fromthe sensor 104 or data communication server 108 via Bluetooth radio,NFC, and other similar wireless communication tools. In variousembodiments, the data collection server 108 may include memory 116 andone or more processors 118. In at least one embodiment, the memory 116may include a storage database. In some embodiments, the memory 116 maystore historical deobfuscated glucose data, along with associated timestamps, identifiers, and other associated data, for the user. In someembodiments, the data collection server 108 may retrieve the historicaldeobfuscated glucose data from the memory 116 for determining missingglucose exposure data.

In many embodiments, the data collection server 108 may be operativelyconnected to a computing device 124.

In one or more embodiments, the data collection server 108 and/or device106 may receive the obfuscated glucose data from the sensor 104. Inseveral embodiments, the data collection server 108 and/or device 106may receive the obfuscated glucose data from the sensor 104 at aparticular interval. In at least one embodiment, the particular intervalmay be a range of time from one second to one hour. For example, thedata collection server 108 and/or device 106 may receive obfuscatedglucose data from the sensor 104 every second, or may receive theobfuscated glucose data from the sensor once per hour, or any otherinterval therebetween.

In multiple embodiments, the sensor 104 may collect glucose data fromthe glucose sensor 120 over a predetermined interval, but, instead oftransmitting each glucose data upon receiving the electronic signal fromthe glucose sensor 120, the sensor 104 may store the glucose data andbatch the glucose data for transmitting. In many embodiments, the sensor104 may transmit a batch of glucose data to the device 106 or datacollection server 108 after a specific amount of time or after aspecific amount of glucose data has been received from the glucosesensor 120. For example, in this embodiment, the glucose sensor 120 maysend the sensor 104 the glucose data at a constant rate (e.g., one persecond), but the sensor 104 may collect multiple glucose data from theglucose sensor 120 and only transmit the glucose data to the device 106or data collection server 108 once the sensor 104 has received aspecific amount of glucose data (e.g., every five, ten, or twentyglucose data) from the glucose sensor 120 (e.g., in a batch). In oneembodiment, the predetermined interval may be the amount of time betweenthe glucose sensor 120 sending indications to the sensor 104.

In many embodiments, the data collection server 108 and/or device 106may store the obfuscated glucose data in the memory 116. In severalembodiments, the device 106, utilizing the one or more processors 126,and/or the data collection server 108, utilizing the one or moreprocessors 118, may deobfuscate the obfuscated glucose data. In one ormore embodiments, once the obfuscated glucose data is deobfuscated, the(deobfuscated) glucose data may be stored in the memory 116 and/or inthe device 106 (as will be understood, the device 106 may include localmemory/storage). In at least one embodiment, the glucose data may bestored in the memory 116 and/or the device 106 for a certain amount oftime, including, but not limited to, ninety days.

In various embodiments, the system 100 may also include a GPS satellite110. In one or more embodiments, the GPS satellite 110 may be utilizedto track the location of the device 106. In many embodiments, the system100 may utilize the location of the device 106 as movement data for theuser, and calculate a distance traveled by the user based on changinglocations of the device 106. In at least one embodiment, the glucosedisclosure system may also calculate the speed of the user by dividingthe distance traveled by the user by the time it took for the user totravel the distance.

According to particular embodiments, the device 106 may include anysuitable additional components, such as, but not limited to, agyroscope, accelerometer, heart rate sensor, pulse oximeter, etc. In oneembodiment, the device 106 and/or data collection service 108 maydetermine a step count or other suitable data for a user wearing thedevice 106.

As shown in FIG. 2A, an exemplary glucose exposure process 200 isdescribed, according to one embodiment of the present disclosure. Invarious embodiments, a user may first connect the sensor 104 to theuser's body, such that the glucose sensor 120 is interacting with theinterstitial fluid or otherwise determining a level of glucose withinthe patient's blood.

According to one embodiment, at step 202 of process 200, the system 100may receive the obfuscated glucose data from the transmitter 122 of thesensor 104 at a particular interval. In at least one embodiment, theobfuscated glucose data is derived from the glucose sensor 120interacting with the user, and specifically, with the user'sinterstitial fluid. In one or more embodiments, the system 100 mayreceive the obfuscated glucose data at the device 106 or the datacollection server 108.

In multiple embodiments, the particular interval is a time interval bywhich the system 100 receives the obfuscated glucose data from thesensor 104. In many embodiments, the particular interval may one second,or may be one day, or any time therebetween. For example, in oneembodiment, the particular interval may be 15 minutes, 30 minutes, 1hour, 1 day, etc.

In several embodiments, the particular interval may be the time intervalbetween the glucose sensor 120 transmitting indications to the sensor104. In this embodiment, when the sensor 104 receives an indication ofglucose data from the glucose sensor 120, the sensor 104 may also recorda timestamp and associate the time stamp with the received glucose data.Continuing in this embodiment, the sensor 104 may thereafter transmitthe glucose data and associated time stamp to the device 106 and/or dataconnection server 108.

At step 204, in various embodiments, the system 100 may deobfuscate theobfuscated glucose data received from the sensor 104. In one or moreembodiments, the device 106 or the data collection server 108 maydeobfuscate the obfuscated glucose data. As will be understood fromdiscussions here, the sensor 104 may obfuscate glucose data viaencryption, hashing, steganography, etc. In some embodiments, once thedevice 106 receives the obfuscated (or encrypted) glucose, the device106 may deobfuscate, decrypt, or otherwise decode the glucose data. Inat least one embodiment, the device 106 may transmit the obfuscated tothe data collection server 108 for deobfuscation.

At step 206, in multiple embodiments, the system 100 may determine aglucose exposure over an interval of time based on the glucose data atthe particular time interval. In several embodiments, the glucoseexposure over the interval of time may be an average of the receivedglucose data at the particular interval over the course of the intervalof time. In one or more embodiments, the interval of time may be thesame amount of time as the particular interval, or may be a longeramount of time such that the glucose exposure may be based on more data.In at least one embodiment, the interval of time may be fifteen, thirty,or sixty minutes, a number of hours (see example below regarding arunning total), or some other amount of time. For example, in oneembodiment, the particular interval may be fifteen minutes, and theinterval of time may be sixty minutes, such that the system 100 receivesglucose data four times within the interval of time. Continuing withthis example, the received glucose data over the sixty minute intervalof time may be 90, 92.5, 97.5, and 100 (in units of measurement), whichaverages to a glucose exposure of 95 units of measurement over the sixtyminute interval of time.

In a further embodiment, the system 100 may calculate a running total ofglucose exposure through a twenty-four hour day by adding the determinedglucose exposures over the intervals of time (or a single interval oftime might be the time of the running total) throughout the twenty-fourhours in a day. For example, in one embodiment, if the interval of timeis sixty minutes, and the twenty-four hour day begins at midnight (00:00AM), the system 100 may add each glucose exposure over the interval ofsixty minutes over the course of the twenty-four hour day, so that, at aparticular hour (e.g., 9:00 AM), the system 100 may determine the totalglucose exposure for the user for the day at 9:00 AM.

In an alternate embodiment, the system 100 may utilize a weightedaverage for determining the glucose exposure over the interval of time.In this alternate embodiment, the system 100 may give more weight to theglucose data received closer to the end of the interval of time and lessweight to the glucose data received nearer to the beginning of theinterval of time, so that the glucose exposure over the interval of timeis closer to the current glucose exposure at the end of the interval oftime. For example, in this alternate embodiment, if the system 100received, in order, the glucose data at the particular interval of 90,92.5, 97.5, and 100 (in units of measurement such as, e.g., inmilligrams) over the interval of time, the weighted average may begreater than the actual average 95 of the glucose data.

As described in step 208, in various embodiments, the system 100 maydetermine a target glucose level for a particular hour. In one or moreembodiments, and as shown in more detail in FIG. 2B, the target glucoselevel for the particular hour may be the amount of glucose exposure theuser is trying to attain for the particular hour. As discussed in moredetail below, in some embodiments, step 208 includes dividing a dailyglucose exposure limit by 24 to get a glucose exposure limit per hour(step 212) and multiplying the glucose exposure limit per hour by anumerical expression of the particular hour (step 214). In someembodiments, the target glucose level for the particular hour may be alimit of glucose exposure that the user is trying not to exceed. In atleast one embodiment, the particular hour may be a specific time duringa twenty-four hour period. For example, in one embodiment, theparticular hour may be 9:00 AM.

As described in step 210, in multiple embodiments, the system 100 maydisplay the glucose exposure as a proportion of the target glucose levelfor the particular hour. In at least one embodiment, the system 100 maycompare the running total of the glucose exposure for the user at theparticular hour to the target glucose level for the particular hour. Inan alternate embodiment, the system 100 may compare the glucose exposureover the interval of time to the glucose exposure limit per hour.

For example, in several embodiments, if the interval of time is sixtyminutes, then the system 100 will determine the glucose exposure of theuser every sixty minutes. Continuing with the example, in someembodiments, the running total of glucose exposure at a particular hourmay be the sum of the determined glucose exposure data from the previousintervals of time for the day. Still continuing with this example, inmany embodiments, if the interval of time is sixty minutes, the runningtotal of glucose exposure at 10:00 AM may be the sum of the determinedglucose exposures from 1:00 AM, 2:00 AM, 3:00 AM . . . 10:00 AM. Stillcontinuing with this example, in one or more embodiments, if theprevious determined glucose exposures for the day were 63 (00:00 AM),60, 65, 73, 80, 84, 88, 90, 93, 95, and 97 (10:00 AM), then the runningtotal of the glucose exposure at 10:00 AM is 888 units of measurement ofglucose exposure. Continuing with this example, in one embodiment, ifthe target glucose exposure for 10:00 AM is 910 units of measurement oftargeted glucose exposure, the system 100 may display 888 units ofmeasurement of glucose exposure divided by 910 units of measurement oftargeted glucose exposure. In a further embodiment, the system 100 maydisplay the proportion of the glucose exposure to the target glucoseexposure as a percentage.

In various embodiments, the system 100 may determine a 24-hour averageglucose for the user. In many embodiments, the system may calculate the24-hour average glucose by averaging the user's determined glucoseexposure data from the previous 24-hour period. In some embodiments, the24-hour average glucose may be a rolling average such that the 24-houraverage glucose may be recalculated once an hour or once every intervalof time in which the glucose exposure is determined. For example, in oneembodiment, the 24-hour average glucose at 11:00 AM may be an average ofthe determined glucose exposure data for the previous 24 hours (e.g.,from about 11:00 AM previous day to 11:00 AM current day), while theglucose exposure at the particular hour (11:00 AM) may be the sum of theglucose exposure data from midnight of the current day to 11:00 AM ofthe current day (eleven hours). In at least one embodiment, the systemmay display the 24-hour average glucose. In one or more embodiments, thesystem may compare the current 24-hour period to an immediatelypreceding 24-hour average glucose. In one embodiment, the system maydisplay the difference between the current 24-hour average glucose tothe immediately preceding 24-hour average glucose as a percentage. Insome embodiments, system 100 may store the 24-hour average glucosedeterminations for previous days (e.g., the 24-hour average glucosedetermination from midnight (00:00 AM) to the next midnight (24:00) tobe utilized in additional calculations.

In several embodiments, the system 100 may determine a seven-day averageglucose for the user. In some embodiments, the system may calculate theseven-day average glucose by averaging the user's determined glucoseexposure data from the previous seven-day period. In one or moreembodiments, the system 100 may average the determined 24-hour averageglucose for each of the preceding seven days to determine the seven-dayaverage glucose. In at least one embodiment, the system 100 may displaythe seven-day average glucose. Similarly, in many embodiments, thesystem 100 may determine an average glucose for any time period (e.g.,one month, one year), by averaging 24-hour average glucosedeterminations or seven-day average glucose determinations, or othersimilar glucose exposure calculations. In one embodiment, the system 100may determine a median to calculate the 24-hour average glucose and/orseven-day average glucose.

Turning now to FIG. 2B, an exemplary target glucose exposuredetermination process 208 is shown, according to one embodiment of thepresent disclosure. In multiple embodiments, in order to determine atarget glucose level for a particular hour, the system 100 may firstdivide a glucose exposure limit by 24 to get a glucose exposure limitper hour. In one or more embodiments, the glucose exposure limit may bethe maximum amount of glucose exposure the user desires over the courseof a twenty-four hour day. In at least one embodiment, the user mayinput the glucose exposure limit into the system 100. For example, inone embodiment, the user may input a glucose exposure limit of 1200units of measurement of glucose exposure into the system 100, which thesystem 100 divides by 24 to determine that the glucose exposure limitper hour is 50 units of measurement of glucose exposure.

In various embodiments, as shown in step 214, the system 100 maymultiply the glucose exposure limit per hour by a numerical expressionof the particular hour. In many embodiments, the numerical expression ofthe particular hour correlates to the particular time of day, using a00:00-24:00 time measure for the time of day. For example, in oneembodiment, the particular hour 11:00 AM correlates to 11 for thenumerical expression of the particular hour. In a further embodiment,the minutes portion of the time of day correlates to a decimal for thenumerical expression of the particular hour. For example, in the furtherembodiment, the time of day 5:15 PM correlates to 17.25 for thenumerical expression of the particular hour.

According to one embodiment, as an example of steps 212 and 214, inmultiple embodiments, the user may input a glucose exposure limit of1800 units of measurement. In many embodiments, the system 100 may thendivide the glucose exposure limit by 24, to get a glucose exposure limitper hour of 75 units of measurement per hour. Next, in severalembodiments, if the particular hour is 3:00 PM, the system 100 maymultiple the glucose exposure limit per hour by the numerical expressionof 3:00 PM, which is 15. In one or more embodiments, the system 100 maydetermine that the target glucose exposure for 3:00 PM is 75 units ofmeasurement per hour multiplied by 15 hours, which is 1125 units ofmeasurement of glucose exposure.

In an alternative embodiment, the system 100 may divide the glucoseexposure limit by 1440 to get a target glucose per minute. Continuingwith this alternative embodiment, the system 100 may multiply the targetglucose per minute by a numerical expression of a particular minute. Inthis alternative embodiment, the particular minute may be a specificminute during the day such that the numerical expression of theparticular minute is between 0 and 1440. For example, still continuingin the alternative embodiment, at 1:45 PM, the particular minute isequal to thirteen hours multiplied by 60, and then added to theremaining 45 minutes, which is 825 minutes. In various embodiments, thesystem 100 may display the glucose exposure as a proportion to thetarget glucose exposure for the particular minute. In a furtherembodiment, similar calculations may be done so that the system 100 maydetermine a target glucose exposure for a particular second.

In a further embodiment, the glucose exposure limit may be a function ofthe user's personal information, such as, but not limited to, the user'sheight, weight, body mass index score, average daily exercise, averagedaily glucose exposure, whether the user is preparing for an endurancerace, and/or other similar information. In this embodiment, the system100 may calculate a healthy glucose exposure limit, based on algorithmsand based on the user's personal health goals. For example, in oneembodiment, the user may want to lose weight, so the user may input a“lose weight” goal into the system 100, and based on the user's personalinformation and other factors, the system 100 determines a glucoseexposure limit for the user.

In a further embodiment, the system 100 may import or receive data fromother devices that determine data about a user (or about other users).In various embodiments, the system 100 may store system 100 data in aserver with other system 100 data for other users. In one or moreembodiments, system 100 data may include the user's personalinformation, as well as the user's historical glucose data. In at leastone embodiment, if a user updates the user's personal information andthe update includes a change in body mass index score or weight, thesystem 100 may determine if the user has increased or decreased glucoseexposure. In a further embodiment, the system 100 may deploy machinelearning or AI to optimize the glucose exposure limits for a variety ofuser body types, by using measured glucose data against increases anddecreases in users' weight and body mass index scores.

In various embodiments, the system 100, at step 208, may receive, fromthe user or the system, a glucose exposure limit per hour. In severalembodiments, the glucose exposure limit per hour may be utilized tocalculate the target glucose level for a particular hour by multiplyingthe glucose exposure limit per hour by the numerical expression of theparticular hour, as discussed infra. In one or more embodiments, theglucose exposure limit per hour may be multiplied by 24 to get a 24-hourglucose exposure limit. For example, in one embodiment, the user or thesystem may provide a glucose exposure limit per hour of 80 mg/dL, whichthe system may then multiply by 24 to determine the glucose exposurelimit. Continuing with the example, in some embodiments, if the userchecks his glucose exposure at 3:00 PM, and the 24-hour period began atmidnight (00:00 AM), the system would multiply the glucose exposurelimit per hour by 15 to get the target glucose level for the particularhour (1200 mg/dL). Still continuing with the above example, in manyembodiments, the system may thereafter compare the user's glucoseexposure with the target glucose level for the particular hour, and mayalso display the 24-hour glucose exposure limit. An exemplary glucoseexposure process 300 is shown in FIG. 3A, according to one embodiment ofthe present disclosure. In various embodiments, a user may first connectthe sensor 104 to the user's body, such that the glucose sensor 120 isinteracting with the interstitial fluid or other bodily fluid.

As shown in step 302 of process 300, in multiple embodiments, the system100 may receive a daily glucose exposure limit. In one or moreembodiments, the user may input the daily glucose exposure limit intothe system 100 via the device 106 or the computing device 124. In atleast one embodiment, the daily glucose exposure limit may be themaximum amount of glucose exposure the user desires to receive over thecourse of a twenty-four hour day. In one or more embodiments, a medicalprofessional or other third-party may input the daily glucose exposurelimit into the system 100. In a further embodiment, a physician or othermedical professional may prescribe a specific daily glucose exposurelimit for the user. In some embodiments, the system 100 may calculatethe glucose exposure limit based on weight loss goals, machine learningand artificial intelligence, physical activity goals, or othercalculations.

At step 304, in various embodiments, the system 100 may calculate atarget glucose level for a particular hour. As discussed in more detailbelow (in reference to FIG. 3B), in some embodiments, step 304 includesdividing a glucose exposure limit by 24 to get a glucose exposure limitper hour (step 310) and multiplying the glucose exposure limit per hourby a numerical expression of the particular hour (step 312). In one ormore embodiments, the target glucose level for the particular hour maybe the amount of glucose exposure the user is trying to attain for theparticular hour. In many embodiments, the target glucose level for theparticular hour may be a limit of glucose exposure that the user istrying not to exceed. In at least one embodiment, the particular hourmay be a specific time during a twenty-four hour period. For example, inone embodiment, the particular hour may be 9:00 AM.

As shown in step 306, in several embodiments, the system 100 maydetermine a glucose exposure for the particular hour. As discussed inmore detail below (in reference to FIG. 3C), in some embodiments, step306 includes determining if a Bluetooth radio is connected to the sensor104 (step 314), and if so, receiving, via the Bluetooth radio from thesensor 104, obfuscated glucose data (step 316), deobfuscating the data(step 318), and determining the glucose exposure over a time periodbased on the glucose data received at the predetermined interval (step320), and if the Bluetooth radio is not connected to the sensor,determining an average glucose exposure for the particular hour based onhistorical data (step 322), and using the average glucose exposure forthe particular hour as the glucose exposure for the particular hour(step 324).

At step 308, in many embodiments, the system 100 may display the glucoseexposure calculated at step 306 as a percentage of the target glucoselevel for the particular hour calculated at step 304. For example, inone embodiment, the glucose exposure calculated at step 306 may be 1140at the particular hour, and the target glucose level for the particularhour is 1080, which would be displayed as 105.5%.

As described in FIG. 3B, an exemplary target glucose exposuredetermination process 304 is shown, according to one embodiment of thepresent disclosure. As described in step 310, in multiple embodiments,in order to determine a target glucose level for a particular hour, thesystem 100 may first divide the daily glucose exposure limit by 24 toget a glucose exposure limit per hour. For example, in one embodiment,the user may input a glucose exposure limit of 1200 units of measurementof glucose exposure into the system 100, which the system 100 divides by24 to determine that the glucose exposure limit per hour is 50 units ofmeasurement of glucose exposure.

In various embodiments, as shown in step 312, the system 100 maymultiply the glucose exposure limit per hour by a numerical expressionof the particular hour. In many embodiments, the numerical expression ofthe particular hour correlates to the particular time of day, using a00:00-24:00 time measure for the time of day. For example, in oneembodiment, the particular hour 11:00 AM correlates to 11 for thenumerical expression of the particular hour. In a further embodiment,the minutes portion of the time of day correlates to a decimal for thenumerical expression of the particular hour. For example, in the furtherembodiment, the time of day 5:15 PM correlates to 17.25 for thenumerical expression of the particular hour.

According to one embodiment, as an example of steps 310 and 312, inmultiple embodiments, the user may input a glucose exposure limit of1800 units of measurement. In many embodiments, the system 100 may thendivide the glucose exposure limit by 24, to get a glucose exposure limitper hour of 75 units of measurement per hour. Next, in severalembodiments, if the particular hour is 3:00 PM, the system 100 maymultiple the glucose exposure limit per hour by the numerical expressionof 3:00 PM, which is 15. In one or more embodiments, the system 100 maydetermine that the target glucose exposure for 3:00 PM is 75 units ofmeasurement per hour multiplied by 15 hours, which is 1125 units ofmeasurement of glucose exposure.

In various embodiments, the system 100, at step 304, may receive, fromthe user or the system, a glucose exposure limit per hour. In severalembodiments, the glucose exposure limit per hour may be utilized tocalculate the target glucose level for a particular hour by multiplyingthe glucose exposure limit per hour by the numerical expression of theparticular hour, as discussed infra. In one or more embodiments, theglucose exposure limit per hour may be multiplied by 24 to get a 24-hourglucose exposure limit. For example, in one embodiment, the user or thesystem may provide a glucose exposure limit per hour of 80 mg/dL, whichthe system may then multiply by 24 to determine the glucose exposurelimit. Continuing with the example, in some embodiments, if the userchecks his glucose exposure at 3:00 PM, and the 24-hour period began atmidnight (00:00 AM), the system would multiply the glucose exposurelimit per hour by 15 to get the target glucose level for the particularhour (1200 mg/dL). Still continuing with the above example, in manyembodiments, the system may thereafter compare the user's glucoseexposure with the target glucose level for the particular hour, and mayalso display the 24-hour glucose exposure limit.

FIG. 3C shows an exemplary glucose exposure for a particular hourdetermination process 306, according to one embodiment of the presentdisclosure. In various embodiments, at step 314 of process 306, thesystem 100 determines whether a Bluetooth radio is connected to thesensor 104. In one or more embodiments, the system 100 is wirelesslyconnected to the sensor 104.

In multiple embodiments, as shown in step 316, if a Bluetooth radio isconnected to the sensor 104, then the system 100 receives, via theBluetooth radio from the sensor 104, obfuscated glucose data derivedfrom the glucose sensor 120 interacting with a patient's interstitialfluid at a predetermined interval. In one or more embodiments, thepredetermined interval may be the time between the system 100 receivingglucose data from the sensor 104. In at least one embodiment, thepredetermined interval may be one second, such that the system 100 isessentially constantly receiving obfuscated glucose data. In someembodiments, the predetermined interval may be one hour, such that thesystem 100 receives obfuscated glucose data once per hour. In manyembodiments, the predetermined interval may be one day or multiple days.

In an embodiment, the sensor 104 may transmit a batch ofindications/readings from the glucose sensor 120 to the system 100instead of transmitting each individual indication upon receiving theindication from the glucose sensor 120. For example, in this embodiment,the glucose sensor 120 may send the sensor 104 the glucose data, asdescribed above, at a constant rate (e.g., one indication per second),but the sensor 104 may collect a certain amount of glucose data frommultiple indications (a batch) from the glucose sensor 120 (e.g., everyfive, ten, twenty indications) before transmitting the batch of glucosedata to the system 100. In some embodiments, the batch may include anamount of indications from the glucose sensor 120 to the sensor 104 overa period of time (e.g., amount of indications per hour). Stillcontinuing with this embodiment, the predetermined interval may be theamount of time between the glucose sensor 120 sending indications to thesensor 104 or may be the amount of time between the sensor 104 sendingbatches of indications to the system 100.

As described in step 318, in various embodiments, the system 100deobfuscates the glucose data. In one or more embodiments, the device106, the data collection server 108, or the computing device 124 maydeobfuscate the glucose data. In some embodiments, the device 106 mayreceive the obfuscated glucose data via Bluetooth radio or other similarcommunication device. In at least one embodiment, the device 106, afterreceiving the obfuscated glucose data, may deobfuscate the glucose dataor send the obfuscated glucose data to the data collection server 108,which will deobfuscate the glucose data. In one embodiment, if thedevice 106 sends the obfuscated glucose data to the data collectionserver 108, the data collection server 108 (or the connected computingdevice 124) may deobfuscate the glucose data, and the data collectionserver 108 may thereafter transmit the deobfuscated glucose data to thedevice 106. In many embodiments, once the glucose data is deobfuscated,the system 100 may read and utilize the glucose data.

As shown in step 320, in several embodiments, the system 100 determinesthe glucose exposure over a time period based on the glucose datareceived at the predetermined interval. In at least one embodiment, theglucose exposure over a time period may be an average of the glucosedata received at the predetermined interval over the course of the timeperiod.

In many embodiments, the time period (or time interval) may be anoverall amount of time from which the glucose exposure is beingmeasured. For example, in one embodiment, the system 100 may determinethe glucose exposure at the time 8:00 AM (the particular hour).Continuing with the example, in some embodiments, the time period may befrom 00:00 AM to 8:00 AM, such that the system 100 determines theglucose exposure for the time period.

In another example, in at least one embodiment, the system 100 maydetermine the glucose exposure at the time 8:00 AM (the particularhour), and the time period may be one hour. Continuing with thisexample, the system 100 may determine the glucose exposure for each timeperiod, and determine the glucose exposure at 8:00 AM by summing up theindividual glucose exposures for each hour (or other increment of time)throughout the day. In one or more embodiments, the time period mayrange from one second to one day (such as, e.g., 15 minutes, 30 minutes,1 hour, 3 hours, 1 day, etc.).

For example, in one embodiment, the predetermined interval may be oneminute, and the time period may be thirty minutes, such that the system100 receives glucose data thirty times within the time period.Continuing with the example, in at least one embodiment, the system 100may calculate the average of the thirty glucose data points to determinethe glucose exposure over the period of time. In an alternateembodiment, the system 100 may calculate a weighted average of thethirty glucose data points, such that the later received glucose datapoints have more weight than the earlier received glucose data points.

In a further embodiment, the system 100 may calculate a running total ofglucose exposure through a twenty-four hour day by adding the determinedglucose exposures over the time periods throughout the twenty-fourhours. For example, in one embodiment, if the time period is sixtyminutes, and the twenty-four hour day begins at midnight (00:00 AM), thesystem 100 may add each glucose exposure (in units of measurement) foreach sixty minute time period over the course of the twenty-four hourday, so that, at a particular hour (e.g., 9:00 AM), the system 100 maydetermine the total glucose exposure for the user for the day at 9:00AM.

In one or more embodiments, the system may be configured to compensatefor a disconnected sensor and may use one or more smoothing algorithms(or the like) to fill in or approximate glucose exposure for an hour (orother suitable time period). For example, if a user is sleeping and isnot wearing a sensor, the system may use historical or other data toestimate the user's glucose exposure while the sensor is disconnected.

At step 322, in multiple embodiments, if a Bluetooth radio is notconnected to the sensor 104, the system 100 determines an averageglucose exposure for the particular hour based on historical data. Inthis embodiment, since the system 100 is not connected to the sensor104, the system 100 may not be able to receive current glucose data atthe predetermined interval from the sensor 104. In many embodiments, thesystem 100 may store historical glucose data such that the system 100may retrieve historical glucose data from previous days and utilize thehistorical data in the present average glucose exposure determination.In one or more embodiments, the utilization of the historical dataallows the system 100 to continue to calculate the total glucoseexposure and display the glucose exposure as a percentage of the targetglucose level for the particular hour. In at least one embodiment, thehistorical data may include particular hour information, such that thesystem 100 may incorporate historical data from the same particular houras the particular hour glucose data that is missing due to the system100 not being connected to the sensor 104.

For example, in one embodiment, the Bluetooth radio may not be connectedto the sensor 104 from 2:00 PM to 3:00 PM. Continuing with the example,in several embodiments, the system 100 may retrieve stored historicaldata from 2:00 PM to 3:00 PM from previous days, and average the storedhistorical data for the particular hour to get an average glucoseexposure for the particular hour based on historical data. In analternative embodiment, the system 100 may determine a weighted averagefor the glucose exposure for the particular hour based on historicaldata, such that the more recent historical data is given more weightthan the older historical data, because the more recent historical datais more likely to be more accurate to the actual current glucoseexposure.

In at least one embodiment, if the system 100 does not receive theglucose data from the sensor 104 at the particular interval, the system100 may apply one or more smoothing algorithms once the system 100 isreconnected to the sensor 104, to back fill the missing glucose data. Inone or more embodiments, the one or more smoothing algorithms mayinclude calculating an average glucose exposure based on the glucosedata received before and after the system 100 stopped receiving glucosedata from the sensor 104. For example, in one embodiment, if the system100 did not receive glucose data for one predetermined interval, thesystem 100 may utilize immediately preceding glucose data for at leastone predetermined interval and immediately succeeding glucose data forat least one predetermined interval, and average the at least twoglucose data points together to determine the missing glucose data forthe predetermined interval. In at least one embodiment, the system 100may utilize multiple immediately preceding glucose data points andmultiple immediately succeeding glucose data points to determine themissing glucose data for the predetermined interval. In manyembodiments, the user's glucose exposure does not vary much from onepredetermined interval to the next, so the system 100 is able to take anaverage from the glucose data from preceding and succeeding glucose datato fill in a missing glucose data point with a high level of accuracy.

In one or more embodiments, the system 100 may utilize a combination ofhistorical data and an average of recent data to determine missingglucose data points. For example, in one embodiment, if the system 100is missing a glucose data point for 10:00 AM, the system 100 mayretrieve historical glucose exposure data for 10:00 AM for the user, aswell as calculate an average of recent preceding and succeeding glucosedata, and determine or estimate the missing glucose data point from acombination of the historical glucose exposure data and the average ofrecent preceding and succeeding glucose data. In a further embodiment,the system 100 may also utilize other users' glucose exposure data todetermine missing glucose data points. In this further embodiment, thesystem 100 may recognize other users as similar to the user with missingglucose exposure data, based on similarities in the users' profiles,such as the users' age, gender, height, weight, similarity in glucoseexposure, and other similar factors.

In various embodiments, the system 100 may notify the user if the system100 determines that the user's glucose exposure is higher or lower thanthe target glucose level for a particular hour by a more than a certainpercentage. For example, in at least one embodiment, the system 100 maynotify the user if the user's glucose exposure is ten percent (or more)greater than or less than the user's target glucose level for aparticular hour. In many embodiments, the system 100 may notify the uservia displaying a message on the device 106 or causing a pushnotification, SMS message, email, or other similar communication todisplay on or transmit to a secondary device.

In one or more embodiments, if the user's glucose exposure is greaterthan the user's glucose target for a particular hour, the system 100 maymake recommendations to the user so that the user's glucose exposure maydecrease in forthcoming hour(s). In some embodiments, therecommendations may include, but are not limited to, eatinglow-carbohydrate foods for the user's next meal, exercising, including aspecific intensity level of exercising (such as, e.g., walking, jogging,running) taking insulin (for diabetic users), including rapid-actinginsulin, short-acting insulin, intermediate-acting insulin,mixed-insulin, and long-acting insulin, or a combination ofrecommendations. In one embodiment, if the user has indicated to thesystem that the user's goal is to intake carbohydrates in preparationfor future physical activity (such as, e.g., running a marathon), thesystem 100 may not notify the user if the user's glucose exposureexceeds the glucose target for a particular hour.

In multiple embodiments, the if the user's glucose exposure is less thanthe user's target glucose level for a particular hour by a certainamount, the system 100 may recommend the user take an action to increasethe user's glucose exposure. In some embodiments, situations in whichthe system 100 may notify the user to increase the user's glucoseexposure may include, but is not limited to, the user ingestingcarbohydrates in preparation for a physical activity (such as, e.g., atriathlon), hypoglycemia, or other situations in which the user'sglucose exposure is lower than the target glucose level for a particularhour. In many embodiments, the system 100 may recommend to a user to eata carbohydrate-rich meal or snack to increase the user's glucoseexposure if the user's glucose exposure is less than the target glucoselevel for a particular hour. For example, in one embodiment, a user mayindicate to the system 100 that the user is attempting to reach orsurpass the target glucose level for a particular hour in preparation torun a marathon, and so if the user's glucose exposure is five percentlower than the target glucose level for a particular hour, the system100 may notify the user and recommend the user ingest carbohydrates. Inat least one embodiment, the system 100 may recommend the user seekmedical treatment, such as but not limited to, going to an emergencyroom or calling an ambulance, or other similar medical treatment, if theuser's glucose exposure is low enough to be considered hypoglycemic.

As shown in FIG. 4, an exemplary glucose exposure diagnostic andtreatment process 400 is described, according to one embodiment of thepresent disclosure. In various embodiments, a user may first connect thesensor 104 to the user's body, such that the glucose sensor 120 isinteracting with the interstitial fluid or otherwise determining a levelof glucose within the patient's blood. In some embodiments, process 400includes receiving glucose exposure data derived from a filamentinteracting with interstitial fluid at a particular interval (step 402),determining a glucose exposure over an interval of time based on theglucose data at the particular interval (step 404), dividing a glucoseexposure limit by 24 to get a glucose exposure limit per hour (step406), multiplying the glucose exposure limit per hour by a numericalexpression of the particular hour (408), and administering treatment tothe patient to lower the patient's glucose exposure to at or below theglucose exposure limit before the end of a 24-hour period (step 410).Steps 402, 404, 406, and 408 are substantially similar to steps 202,206, 212, and 216 as described herein. For the sake of brevity, steps402, 404, 406, and 408 will not be described here, but reference is madeto steps 202, 206, 212, and 216 for descriptions of these steps.

As shown in step 410, in several embodiments, the system 100 mayrecommend, prescribe, and/or administer treatment to a patient to lowerthe patient's glucose exposure to at or below the glucose exposure limit(e.g., before the end of a 24-hour period). In many embodiments,suitable treatments may include prescribing and/or administeringmedications, exercising, dieting, or other similar treatments.

In multiple embodiment, the system 100 may be utilized to treatdiabetes. In this embodiment, the system 100 may administer or recommendthe patient be administered insulin or recommend the patient beadministered insulin if the patient's glucose exposure exceeds thetarget glucose level for a particular hour.

In various embodiments, the system 100 may be utilized to treatnonalcoholic fatty liver disease and/or nonalcoholic steatohepatitis. Insome embodiments, patients with nonalcoholic fatty liver disease and/ornonalcoholic steatohepatitis may limit glucose intake to decrease theamount of fat stored in the patient's liver. In one or more embodiments,upon a patient's current glucose exposure exceeding the target glucoselevel for a particular hour, the system 100 may administer or recommendthe patient be administered treatments including but not limited to,insulin, insulin-sensitizing drugs (such as, e.g., pioglitazone),vitamin E, exercise, low-carbohydrate diet, and/or weight lossmedication, to treat nonalcoholic fatty liver disease and/ornonalcoholic steatohepatitis.

In many embodiments, the system 100 may be utilized to treat obesity ina patient. In at least one embodiment, limiting glucose exposure maycause an obese patient to lose weight. In some embodiments, upon apatient's current glucose exposure exceeding the target glucose levelfor a particular hour, the patient may be administered weight lossmedications (such as, e.g., semaglutide), dietary advice (such as, e.g.,eating a low-carb meal), recommended exercise, and/or other similartreatments.

In several embodiments, the system 100 may be utilized to prevent apre-diabetic patient to converting to type-II diabetes. In one or moreembodiments, the system 100 may, upon a patient's current glucoseexposure exceeding the target glucose level for a particular hour, thepatient may be administered a low dosage of insulin, weight lossmedication, dietary advice, recommended exercise, and/or other similartreatments.

From the foregoing, it will be understood that various aspects of theprocesses described herein are software processes that execute oncomputer systems that form parts of the system. Accordingly, it will beunderstood that various embodiments of the system described herein aregenerally implemented as specially-configured computers includingvarious computer hardware components and, in many cases, significantadditional features as compared to conventional or known computers,processes, or the like, as discussed in greater detail herein.Embodiments within the scope of the present disclosure also includecomputer-readable media for carrying or having computer-executableinstructions or data structures stored thereon. Such computer-readablemedia can be any available media which can be accessed by a computer, ordownloadable through communication networks. By way of example, and notlimitation, such computer-readable media can comprise various forms ofdata storage devices or media such as RAM, ROM, flash memory, EEPROM,CD-ROM, DVD, or other optical disk storage, magnetic disk storage, solidstate drives (SSDs) or other data storage devices, any type of removablenon-volatile memories such as secure digital (SD), flash memory, memorystick, etc., or any other medium which can be used to carry or storecomputer program code in the form of computer-executable instructions ordata structures and which can be accessed by a computer.

When information is transferred or provided over a network or anothercommunications connection (either hardwired, wireless, or a combinationof hardwired or wireless) to a computer, the computer properly views theconnection as a computer-readable medium. Thus, any such a connection isproperly termed and considered a computer-readable medium. Combinationsof the above should also be included within the scope ofcomputer-readable media. Computer-executable instructions comprise, forexample, instructions and data which cause a computer to perform onespecific function or a group of functions.

Those skilled in the art will understand the features and aspects of asuitable computing environment in which aspects of the disclosure may beimplemented. Although not required, some of the embodiments of theclaimed systems and processes may be described in the context ofcomputer-executable instructions, such as program modules or engines, asdescribed earlier, being executed by computers in networkedenvironments. Such program modules are often reflected and illustratedby flow charts, sequence diagrams, exemplary screen displays, and othertechniques used by those skilled in the art to communicate how to makeand use such computer program modules. Generally, program modulesinclude routines, programs, functions, objects, components, datastructures, application programming interface (API) calls to othercomputers whether local or remote, etc. that perform particular tasks orimplement particular defined data types, within the computer.Computer-executable instructions, associated data structures and/orschemas, and program modules represent examples of the program code forexecuting steps of the methods disclosed herein. The particular sequenceof such executable instructions or associated data structures representexamples of corresponding acts for implementing the functions describedin such steps.

Those skilled in the art will also appreciate that the claimed and/ordescribed systems and methods may be practiced in network computingenvironments with many types of computer system configurations,including personal computers, smartphones, tablets, hand-held devices,multi-processor systems, microprocessor-based or programmable consumerelectronics, networked PCs, minicomputers, mainframe computers, and thelike. Embodiments of the claimed systems and processes are practiced indistributed computing environments where tasks are performed by localand remote processing devices that are linked (either by hardwiredlinks, wireless links, or by a combination of hardwired or wirelesslinks) through a communications network. In a distributed computingenvironment, program modules may be located in both local and remotememory storage devices.

An exemplary system for implementing various aspects of the describedoperations, which is not illustrated, includes a computing deviceincluding a processing unit, a system memory, and a system bus thatcouples various system components including the system memory to theprocessing unit. The computer will typically include one or more datastorage devices for reading data from and writing data to. The datastorage devices provide nonvolatile storage of computer-executableinstructions, data structures, program modules, and other data for thecomputer.

Computer program code that implements the functionality described hereintypically comprises one or more program modules that may be stored on adata storage device. This program code, as is known to those skilled inthe art, usually includes an operating system, one or more applicationprograms, other program modules, and program data. A user may entercommands and information into the computer through keyboard, touchscreen, pointing device, a script containing computer program codewritten in a scripting language or other input devices (not shown), suchas a microphone, etc. These and other input devices are often connectedto the processing unit through known electrical, optical, or wirelessconnections.

The computer that effects many aspects of the described processes willtypically operate in a networked environment using logical connectionsto one or more remote computers or data sources, which are describedfurther below. Remote computers may be another personal computer, aserver, a router, a network PC, a peer device or other common networknode, and typically include many or all of the elements described aboverelative to the main computer system in which the systems and processesare embodied. The logical connections between computers include a localarea network (LAN), a wide area network (WAN), virtual networks (WAN orLAN), and wireless LANs (WLAN) that are presented here by way of exampleand not limitation. Such networking environments are commonplace inoffice-wide or enterprise-wide computer networks, intranets, and theInternet.

When used in a LAN or WLAN networking environment, a computer systemimplementing aspects of the systems and processes is connected to thelocal network through a network interface or adapter. When used in a WANor WLAN networking environment, the computer may include a modem, awireless link, or other mechanisms for establishing communications overthe wide area network, such as the Internet. In a networked environment,program modules depicted relative to the computer, or portions thereof,may be stored in a remote data storage device. It will be appreciatedthat the network connections described or shown are exemplary and othermechanisms of establishing communications over wide area networks or theInternet may be used.

While various aspects have been described in the context of a preferredembodiment, additional aspects, features, and methodologies of theclaimed systems and processes will be readily discernible from thedescription herein, by those of ordinary skill in the art. Manyembodiments and adaptations of the disclosure and claimed systems andprocesses other than those herein described, as well as many variations,modifications, and equivalent arrangements and methodologies, will beapparent from or reasonably suggested by the disclosure and theforegoing description thereof, without departing from the substance orscope of the claims. Furthermore, any sequence(s) and/or temporal orderof steps of various processes described and claimed herein are thoseconsidered to be the best mode contemplated for carrying out the claimedsystems and processes. It should also be understood that, although stepsof various processes may be shown and described as being in a preferredsequence or temporal order, the steps of any such processes are notlimited to being carried out in any particular sequence or order, absenta specific indication of such to achieve a particular intended result.In most cases, the steps of such processes may be carried out in avariety of different sequences and orders, while still falling withinthe scope of the claimed systems and processes. In addition, some stepsmay be carried out simultaneously, contemporaneously, or insynchronization with other steps.

The embodiments were chosen and described in order to explain theprinciples of the claimed systems and processes and their practicalapplication so as to enable others skilled in the art to utilize thesystems and processes and various embodiments and with variousmodifications as are suited to the particular use contemplated.Alternative embodiments will become apparent to those skilled in the artto which the claimed systems and processes pertain without departingfrom their spirit and scope. Accordingly, the scope of the claimedsystems and processes is defined by the appended claims rather than theforegoing description and the exemplary embodiments described therein.

1.-20. (canceled)
 21. A method comprising: receiving indications ofglucose levels of a patient via a filament interacting with interstitialfluid; determining an average glucose level for a particular hour;determining a current glucose exposure for the particular hour bysumming the average glucose level for the particular hour with asummation of average glucose levels of the patient for hours precedingthe particular hour in a 24-hour period; determining that the currentglucose exposure for the particular hour exceeds a target glucose levelfor the particular hour; and administering treatment to the patient tolower a glucose exposure level of the patient.
 22. The method of claim21, wherein the treatment comprises insulin.
 23. The method of claim 22,wherein the treatment comprises one or more of the group comprisingrapid-acting insulin, short-acting insulin, intermediate-acting insulin,mixed-insulin, or long-acting insulin.
 24. The method of claim 21,wherein the treatment comprises pioglitazone.
 25. The method of claim21, wherein the treatment comprises one or more weight loss drugs. 26.The method of claim 21, wherein the treatment comprises one or moredrugs to treat nonalcoholic fatty liver disease or nonalcoholicsteatohepatitis.
 27. The method of claim 21, further comprising:determining the target glucose level for the particular hour bymultiplying a glucose exposure limit per hour by a numericalrepresentation of the particular hour, wherein the glucose exposurelimit per hour is based at least in part on historical glucose exposuredata for the patient.
 28. The method of claim 21, wherein theindications of the glucose levels of the patient are received at aparticular interval of 15 minutes.
 29. The method of claim 28, whereindetermining the average glucose level for the particular hour comprisesaveraging four of the indications of the glucose levels of the patientreceived during the particular hour.
 30. The method of claim 21, whereinadministering the treatment lowers the glucose exposure level of thepatient to at or below a 24-hour glucose exposure limit, wherein the24-hour glucose exposure limit comprises a summation of a glucoseexposure limit per hour for 24 hours.
 31. The method of claim 30,wherein the average glucose levels of the patient for hours precedingthe particular hour in the 24-hour period comprises at least one averageglucose level based on historical data.
 32. The method of claim 31,wherein the at least one average glucose level based on historical datacomprises an average of average glucose levels for an hour immediatelypreceding the particular hour and an hour in a different 24-hour period.33. The method of claim 31, wherein the at least one average glucoselevel based on historical data comprises an average of stored glucoseexposure data for the particular hour for different 24-hour periods. 34.A method comprising: receiving a current glucose exposure for aparticular hour, wherein the current glucose exposure for the particularhour is determined by summing an average glucose level for theparticular hour with a summation of average glucose levels of a patientfor hours preceding the particular hour in a 24-hour period, wherein theaverage glucose level is determined from one or more indications of aglucose level of the patient that are derived from a filamentinteracting with interstitial fluid; determining that the currentglucose exposure for the particular hour exceeds a target glucose levelfor the particular hour based on a glucose exposure limit; andadministering treatment to the patient to lower a glucose exposure levelof the patient to at or below the glucose exposure limit.
 35. The methodof claim 34, wherein the glucose exposure limit is a 24-hour glucoseexposure limit.
 36. The method of claim 34, wherein the glucose exposurelimit is a glucose exposure limit for less than 24 hours.
 37. The methodof claim 34, wherein the treatment comprises one or more of the groupcomprising rapid-acting insulin, short-acting insulin,intermediate-acting insulin, mixed-insulin, or long-acting insulin. 38.The method of claim 34, wherein the treatment comprises one or moredrugs to treat nonalcoholic fatty liver disease or nonalcoholicsteatohepatitis.